自主监督的预测学习可以解释皮质层的特异性
Kevin Kermani Nejad1,2, Paul Anastasiades3, Loreen Hertäg4
1Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Nature communications
|July 4, 2025
概括
新皮质使用自我监督学习来预测感官输入,将过去的信息与上下文整合起来. 这种在2/3层 (L2/3) 和5层 (L5) 中观察到的预测机制解释了大脑如何构建内部世界模型.
科学领域:
- 计算神经科学是一种计算神经科学.
- 系统神经科学 系统神经科学
- 机器学习在神经科学中的应用.
背景情况:
- 新皮质对世界的内部表征至关重要,但人们对其了解甚少.
- 现有的模型对所涉及的电路和计算原理缺乏清晰度.
研究的目的:
- 提出一种由自我监督学习启发的新皮层功能计算理论.
- 解释2/3层 (L2/3) 如何整合感官输入和环境进行预测.
- 阐明预测错误在学习和行为中的作用.
主要方法:
- 开发了一个计算模型,其中L2/3使用过去的数据和上下文本来预测感官输入.
- 通过将L2/3预测与L5感官表示进行比较,实现了自我监督的学习.
- 通过使用上下文依赖的时间任务和 in silico 的感觉运动任务验证了模型.
主要成果:
- 该模型准确地预测复杂任务中的感官信息,显示对噪音和阻塞的稳定性.
- 产生了与实验结果相一致的特定层稀疏性模式.
- 模型的预测错误反映了在感觉运动任务期间醒着的小鼠的神经不匹配反应.
结论:
- 多层新皮质促进自我监督的预测学习,使大脑能够预测感官刺激.
- 拟议的模型为皮层特征的计算作用提供了可测试的预测.
- 研究结果表明,预测编码是新皮层计算的基本原则.
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